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    [10]:
    gender race/ethnicity parental level of education lunch test preparation course math score reading score writing score
    0 female group B bachelor's degree standard none 72 72 74
    1 female group C some college standard completed 69 90 88
    2 female group B master's degree standard none 90 95 93
    3 male group A associate's degree free/reduced none 47 57 44
    4 male group C some college standard none 76 78 75 
    <class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 8 columns):
 #   Column                       Non-Null Count  Dtype 
---  ------                       --------------  ----- 
 0   gender                       1000 non-null   object
 1   race/ethnicity               1000 non-null   object
 2   parental level of education  1000 non-null   object
 3   lunch                        1000 non-null   object
 4   test preparation course      1000 non-null   object
 5   math score                   1000 non-null   int64 
 6   reading score                1000 non-null   int64 
 7   writing score                1000 non-null   int64 
dtypes: int64(3), object(5)
memory usage: 62.6+ KB
    [14]:
    math score reading score writing score
    count 1000.00000 1000.000000 1000.000000
    mean 66.08900 69.169000 68.054000
    std 15.16308 14.600192 15.195657
    min 0.00000 17.000000 10.000000
    25% 57.00000 59.000000 57.750000
    50% 66.00000 70.000000 69.000000
    75% 77.00000 79.000000 79.000000
    max 100.00000 100.000000 100.000000
    [16]:
    Index(['gender', 'race/ethnicity', 'parental level of education', 'lunch',
       'test preparation course', 'math score', 'reading score',
       'writing score'],
      dtype='object')
    [18]:
    gender                         0
race/ethnicity                 0
parental level of education    0
lunch                          0
test preparation course        0
math score                     0
reading score                  0
writing score                  0
dtype: int64
    [20]:
    math score reading score writing score
    gender
    female 63.633205 72.608108 72.467181
    male 68.728216 65.473029 63.311203
    [22]:
    math score reading score writing score
    lunch
    free/reduced 58.921127 64.653521 63.022535
    standard 70.034109 71.654264 70.823256
    [24]:
    math score reading score writing score
    test preparation course
    completed 69.695531 73.893855 74.418994
    none 64.077882 66.534268 64.504673
    [30]:
    sns.histplot(df["reading score"], kde=True)
    plt.title("Reading Score Distribution")
    plt.show()
    [32]:
    df.groupby("test preparation course")[["math score", "reading score", "writing score"]].mean().plot(kind="bar")
    plt.title("Average Scores vs Test Preparation")
    plt.ylabel("Average Score")
    plt.show()
    [34]:

    df.groupby("gender")[["math score", "reading score", "writing score"]].mean()

    [34]:
    math score reading score writing score
    gender
    female 63.633205 72.608108 72.467181
    male 68.728216 65.473029 63.311203
    [ ]:

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